vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

remember to copy back the last_eigenvector

Browse source
This commit is contained in:
ngxson 2024-06-11 12:40:07 +02:00
parent 1a088fb0a5
commit 163916864c
1 changed files with 15 additions and 11 deletions
Download patch file Download diff file Expand all files Collapse all files

26
examples/control-vector-generator/pca.hpp
View file

@ -62,9 +62,6 @@ struct pca_model {
struct ggml_tensor * dev_square;
struct ggml_tensor * dev_eigenvector;
// tensors to store output data on host
struct ggml_tensor * host_eigenvector;
pca_model(struct ggml_tensor * t_input) {
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
@ -129,17 +126,16 @@ struct pca_model {
}
// init host context
struct ggml_init_params host_params = {
/*.mem_size =*/ (n_embd * sizeof(float) + ggml_tensor_overhead()) * 2u,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
ctx_host = ggml_init(host_params);
host_eigenvector = ggml_new_tensor_1d(ctx_host, GGML_TYPE_F32, n_embd);
//struct ggml_init_params host_params = {
// /*.mem_size =*/ (n_embd * sizeof(float) + ggml_tensor_overhead()) * 2u,
// /*.mem_buffer =*/ NULL,
// /*.no_alloc =*/ false,
//};
//ctx_host = ggml_init(host_params);
//host_eigenvector = ggml_new_tensor_1d(ctx_host, GGML_TYPE_F32, n_embd);
}
~pca_model() {
ggml_free(ctx_host);
ggml_free(ctx);
ggml_backend_buffer_free(buffer);
ggml_backend_free(backend);
@ -299,6 +295,14 @@ static void power_iteration(
ggml_backend_tensor_set(model.dev_square, tmp_buf.data(), 0, tmp_buf.size());
}
{
// copy last eigen vector and store as input for next iteration
GGML_ASSERT(last_eigenvector != NULL);
std::vector<uint8_t> tmp_buf(ggml_nbytes(last_eigenvector));
ggml_backend_tensor_get(last_eigenvector, tmp_buf.data(), 0, tmp_buf.size());
ggml_backend_tensor_set(model.dev_eigenvector, tmp_buf.data(), 0, tmp_buf.size());
}
printf("%s: layer %d/%d, iteration: %d / total: %d (batch = %d) ...\n",
__func__, params.i_layer+1, params.n_layers, iter, n_iters, params.n_batch);
}